Tootfinder

Myrtle Beach, SC at dawn this morning.
Nikon d3400
18-55mm zoom lens 64x neutral density filter
ISO100
Aperture priority at f29
#photography #ocean #sunrise

An eigenvalue interlacing approach to Garland's method
Alan Lew
https://arxiv.org/abs/2508.17279 https://arxiv.org/pdf/2508.17279

An eigenvalue interlacing approach to Garland's method
Let $X$ be a pure $d$-dimensional simplicial complex. For $0\le k\le d$, let $X(k)$ be the set of $k$-dimensional faces of $X$, let $\tilde{L}_k(X)$ be the $k$-dimensional weighted total Laplacian operator on $X$, and let $\tilde{H}_k(X;\mathbb{R})$ be its $k$-dimensional reduced homology group with real coefficients. For $σ\in X$, let $\text{lk}(X,σ)$ be the link of $σ$ in $X$. For a matrix $M$, we denote by $\text{Spec}(M)$ the multi-set containing all the eigenvalues of $M$. We show that,…

AetherCode: Evaluating LLMs' Ability to Win In Premier Programming Competitions
Zihan Wang, Jiaze Chen, Zhicheng Liu, Markus Mak, Yidi Du, Geonsik Moon, Luoqi Xu, Aaron Tua, Kunshuo Peng, Jiayi Lu, Mingfei Xia, Boqian Zou, Chenyang Ran, Guang Tian, Shoutai Zhu, Yeheng Duan, Zhenghui Kang, Zhenxing Lin, Shangshu Li, Qiang Luo, Qingshen Long, Zhiyong Chen, Yihan Xiao, Yurong Wu, Daoguang Zan, Yuyi Fu, Mingxuan Wang, Ming Ding

AetherCode: Evaluating LLMs' Ability to Win In Premier Programming Competitions
Competitive programming has emerged as a critical benchmark for evaluating the reasoning and coding capabilities of Large Language Models (LLMs). Despite impressive progress on existing benchmarks, we argue that current evaluations overstate model proficiency, masking a substantial gap between LLMs and elite human programmers. This gap arises from two key limitations: insufficient difficulty and scope of benchmark problems, and evaluation bias from low-quality test cases. To address these short…

Bienvenido a la internet de la IA, las redes sociales cerradas de las big-tech. Enshitification.
Re: https://x.com/cerebroatomico/status/1971240494979318258

SpecVLM: Enhancing Speculative Decoding of Video LLMs via Verifier-Guided Token Pruning
Yicheng Ji, Jun Zhang, Heming Xia, Jinpeng Chen, Lidan Shou, Gang Chen, Huan Li
https://arxiv.org/abs/2508.16201 …

SpecVLM: Enhancing Speculative Decoding of Video LLMs via Verifier-Guided Token Pruning
Video large language models (Vid-LLMs) have shown strong capabilities in understanding video content. However, their reliance on dense video token representations introduces substantial memory and computational overhead in both prefilling and decoding. To mitigate the information loss of recent video token reduction methods and accelerate the decoding stage of Vid-LLMs losslessly, we introduce SpecVLM, a training-free speculative decoding (SD) framework tailored for Vid-LLMs that incorporates s…

Last week I gave a talk at the IPTC’s “Photo Metadata Conference”. Here’s a blog-ified version, with lots of juicy digging into exactly what works and what’s broken in C2PA in 2025. Also strong opinions about provenance. And a link to the YouTube recording: https://www.tbray.org/ongoing/When/20…

C2PA Investigations

BurstEngine: an Efficient Distributed Framework for Training Transformers on Extremely Long Sequences of over 1M Tokens
Ao Sun, Weilin Zhao, Xu Han, Cheng Yang, Zhiyuan Liu, Chuan Shi, Maosong sun
https://arxiv.org/abs/2509.19836

BurstEngine: an Efficient Distributed Framework for Training Transformers on Extremely Long Sequences of over 1M Tokens
Existing methods for training LLMs on long-sequence data, such as Tensor Parallelism and Context Parallelism, exhibit low Model FLOPs Utilization as sequence lengths and number of GPUs increase, especially when sequence lengths exceed 1M tokens. To address these challenges, we propose BurstEngine, an efficient framework designed to train LLMs on long-sequence data. BurstEngine introduces BurstAttention, an optimized distributed attention with lower communication cost than RingAttention. BurstAt…

Differential-Integral Neural Operator for Long-Term Turbulence Forecasting
Hao Wu, Yuan Gao, Fan Xu, Fan Zhang, Qingsong Wen, Kun Wang, Xiaomeng Huang, Xian Wu
https://arxiv.org/abs/2509.21196

Differential-Integral Neural Operator for Long-Term Turbulence Forecasting
Accurately forecasting the long-term evolution of turbulence represents a grand challenge in scientific computing and is crucial for applications ranging from climate modeling to aerospace engineering. Existing deep learning methods, particularly neural operators, often fail in long-term autoregressive predictions, suffering from catastrophic error accumulation and a loss of physical fidelity. This failure stems from their inability to simultaneously capture the distinct mathematical structures…

A statistical study of type II outbursts of XRPs: Brighter accreting pulsars rotate faster
Shan-Shan Weng, Xiao-Tian Xu, Han-Long Peng, Yu-Jing Xu, Yan Zhang, Ying-Han Mao, Xiang-Dong Li, Jing-Zhi Yan, Qing-Zhong Liu
https://arxiv.org/abs/2509.21103

A statistical study of type II outbursts of XRPs: Brighter accreting pulsars rotate faster
X-ray pulsars (XRPs) consist of a magnetized neutron star (NS) and an optical donor star. The NS accretes matter from the donor star producing pulsed X-ray emission. In most cases the donor stars are Be stars, and accretion is episodic, that is, the NSs are generally X-ray dim, but occasionally experience outbursts. Here, we carry out a statistical study with the X-ray monitoring data, and obtain strong correlations between the spin periods of the NSs and the outburst parameters for the first t…

SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines
Yizhou Wang, Chen Tang, Han Deng, Jiabei Xiao, Jiaqi Liu, Jianyu Wu, Jun Yao, Pengze Li, Encheng Su, Lintao Wang, Guohang Zhuang, Yuchen Ren, Ben Fei, Ming Hu, Xin Chen, Dongzhan Zhou, Junjun He, Xiangyu Yue, Zhenfei Yin, Jiamin Wu, Qihao Zheng, Yuhao Zhou, Huihui Xu, Chenglong Ma, Yan Lu, Wenlong Zhang, Chunfeng Song, Philip Torr, Shixiang Tang, Xinzhu Ma, Wanli Ouyang, Lei Bai

SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines
We present a scientific reasoning foundation model that aligns natural language with heterogeneous scientific representations. The model is pretrained on a 206B-token corpus spanning scientific text, pure sequences, and sequence-text pairs, then aligned via SFT on 40M instructions, annealed cold-start bootstrapping to elicit long-form chain-of-thought, and reinforcement learning with task-specific reward shaping, which instills deliberate scientific reasoning. It supports four capability famili…